Name
Title | ||
|---|---|---|
Non-fragile mixed H∞ and passive asynchronous state estimation for Markov jump neural networks with randomly occurring uncertainties and sensor nonlinearity. |
Abstract | ||
|---|---|---|
This paper is concerned with the non-fragile mixed H∞ and passive asynchronous state estimation problem for uncertain discrete-time Markov jump neural networks (MJNNs). Both the uncertainties of system and the sensor nonlinearity are considered to be randomly occurring which are governed by a set of Bernoulli distributed white sequences. Since inaccuracies or uncertainties may occur in the designed state estimator and the complete mode synchronization between plant and state estimator is hardly possible, a non-fragile asynchronous state estimator design method is presented. By using an optimize matrix decoupling approach and Lyapunov-Krasovskii methodology, some sufficient conditions for the existence of non-fragile mixed H∞ and passive asynchronous state estimator are proposed. A numerical example is presented to demonstrate the effectiveness of our proposed method. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1016/j.neucom.2016.08.112 | Neurocomputing |
Keywords | Field | DocType |
Markov jump neural networks,Randomly occurring uncertainties,Mixed H∞ and passive asynchronous state estimation,Sensor nonlinearity | Asynchronous communication,Synchronization,Nonlinear system,Control theory,Markov chain,Decoupling (cosmology),Jump,Artificial neural network,Mathematics,Bernoulli's principle | Journal |
Volume | ISSN | Citations |
227 | 0925-2312 | 3 |
PageRank | References | Authors |
0.37 | 23 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Shicheng Huo | 1 | 45 | 2.25 |
| mengshen chen | 2 | 36 | 2.89 |
| Hao Shen | 3 | 1074 | 69.50 |